Lie Detector With The Analysis Of The Change Of Diameter Pupil and The Eye Movement Use Method Gabor Wavelet Transform and Decision Tree

Labibah, Zuhrah; Nasrun, Muhammad; Setianingsih, Casi

doi:10.1109/iotais.2018.8600918

Cited by 10 publications

(10 citation statements)

References 4 publications

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“…The study showed that pupil variation has a significant impact on the control of an endoscope. In a study by Labibah et al (2018), the authors constructed an experiment to determine someone is lying or not using the changes of pupil diameter and the movement of the eyeball, which is left or right. The determination of lies is classified using a decision tree algorithm.…”

Section: Resultsmentioning

confidence: 99%

Eye-Tracking Feature Extraction for Biometric Machine Learning

2022

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ContextEye tracking is a technology to measure and determine the eye movements and eye positions of an individual. The eye data can be collected and recorded using an eye tracker. Eye-tracking data offer unprecedented insights into human actions and environments, digitizing how people communicate with computers, and providing novel opportunities to conduct passive biometric-based classification such as emotion prediction. The objective of this article is to review what specific machine learning features can be obtained from eye-tracking data for the classification task.MethodsWe performed a systematic literature review (SLR) covering the eye-tracking studies in classification published from 2016 to the present. In the search process, we used four independent electronic databases which were the IEEE Xplore, the ACM Digital Library, and the ScienceDirect repositories as well as the Google Scholar. The selection process was performed by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search strategy. We followed the processes indicated in the PRISMA to choose the appropriate relevant articles.ResultsOut of the initial 420 articles that were returned from our initial search query, 37 articles were finally identified and used in the qualitative synthesis, which were deemed to be directly relevant to our research question based on our methodology.ConclusionThe features that could be extracted from eye-tracking data included pupil size, saccade, fixations, velocity, blink, pupil position, electrooculogram (EOG), and gaze point. Fixation was the most commonly used feature among the studies found.

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Section: Resultsmentioning

confidence: 99%

Eye-Tracking Feature Extraction for Biometric Machine Learning

2022

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“…The sensors described in the public dataset [28] are the horizontal mastoids. Electro-oculogyric (EOG signals) were logged from electrodes positioned above the both eyes.…”

Section: Data Description and Preprocessingmentioning

confidence: 99%

“…We aim to develop new method for deep learning based lie detection model. Deep learning attention (DL-Attention) models are used extensively in pattern recognition [28], handwritten recognition [29][30][31]. DL Attention is utilized for grid-like input topology.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for EEG-Based Lie Detection Test Using Spatial and Temporal Aspects

AlArfaj¹,

Mahmoud²

2022

Computers, Materials &Amp; Continua

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Lie detection test is highly significant task due to its impact on criminology and society. Computerized lie detection test model using electroencephalogram (EEG) signals is studied in literature. In this paper we studied deep learning framework in lie detection test paradigm. First, we apply a preprocessing technique to utilize only a small fragment of the EEG image instead of the whole image. Our model describes a temporal feature map of the EEG signals measured during the lie detection test. A deep learning attention model (V-TAM) extracts the temporal map vector during the learning process. This technique reduces computational time and lessens the overfitting in Deep Learning architectures. We propose a Cascading attention model with a deep learning convolutional neural network (CNN). V-TAM model extracts local features and global features in separate paths spatial and temporal. Also, to enhance the EEG segmentation precision, a novel Visual-Temporal Attention Model (V-TAM) is proposed. The accuracy was evaluated using data measured from a sensor from a public dataset of 9512 subjects during fifteen minutes lie detection task. We compared our model with three recent published models. Our proposed model attained the highest performance of (98.5%) with (p < 0.01). The visual-temporal model of the proposed platform shows an optimized balance between prediction accuracy and time efficiency. Validation investigation were performed to prove the correctness and reliability of the proposed method through sizing of the input data, proving its effectiveness in attaining satisfactory performance by using only a smaller size input data.

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“…MFCCs are computed by summing up the weighted log energy magnitudes in a band around a center frequency as shown in (1), where = 1, 2, … the number of cepstral coefficients, is equal to the number of band index and Sk represents the Hamming window function used.…”

Section: A Cepstrum Feature Development and Extractionmentioning

confidence: 99%

“…Deception is intentionally causing an individual to accept falsehood as one that is true. Psychologically speaking someone is being deceptive when subconscious or conscious movements occur including the shortened length of speech, a flushed face, changes in the voice frequency, avoidant eye contact, changes in the diameter of the eye pupil, and a more rigid body [1]. Deception is incorporated in everyday interactions, yet it is challenging for untrained and trained professionals [2]- [3] to accurately detect it without the use of intrusive measures.…”

Section: Introductionmentioning

confidence: 99%

Use of Machine Learning for Deception Detection From Spectral and Cepstral Features of Speech Signals

Fernandes

Ullah

2021

IEEE Access

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In this research, four unique nonlinear speech features are extracted and analyzed to study the dissimilarity pattern between when the speaker is being deceitful and truthful based on how human speech is perceived. The speaker was under stress in a police interrogation where two ground truth and two deceitful responses were recorded during three different times of the day. Using the audio recordings from all three sessions, the cepstral features and spectral energy features are extracted. Cepstral features are the Mel frequency cepstrum coefficient, from where the delta cepstrum and the time-difference cepstrum features are developed. On the other hand, the spectral energy features are the energy of Bark band energy from where the delta energy and the time-difference energy features are developed. The Levenberg-Marquardt classification method and the long short-term memory classification method are then applied to evaluate the accuracy of detecting deception based on the nine unique training and testing combinations of the three different sessions and their extracted cepstrum and spectral energy features. In addition, the principal component analysis is applied to reduce the dimensionality from the extracted features for further improvement. The projected principal components of the four types of features showed improved accuracy in order to distinguish between truthful and deceptive speech pattern. After incorporating with principal component analysis, the long short-term memory classification method with time-difference spectral energy feature shows the highest recognition rate compared to Levenberg-Marquardt algorithm with other cepstral and spectral features.

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Lie Detector With The Analysis Of The Change Of Diameter Pupil and The Eye Movement Use Method Gabor Wavelet Transform and Decision Tree

Cited by 10 publications

References 4 publications

Eye-Tracking Feature Extraction for Biometric Machine Learning

Eye-Tracking Feature Extraction for Biometric Machine Learning

A Deep Learning Model for EEG-Based Lie Detection Test Using Spatial and Temporal Aspects

Use of Machine Learning for Deception Detection From Spectral and Cepstral Features of Speech Signals

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